Evaluation of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

Y. Shen; C. Archambeau; D. Cornford; M. Opper; J. Shawe-Taylor; R. Barillec

doi:10.1109/MLSP.2007.4414324

Evaluation of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

Y. Shen^*, C. Archambeau, D. Cornford, M. Opper, J. Shawe-Taylor, R. Barillec

^*Corresponding author for this work

Computer Science Research Group

Research output: Chapter in Book/Published conference output › Conference publication

3 Citations (Scopus)

9 Downloads (Pure)

Abstract

In recent work we have developed a novel variational inference method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the Variational Gaussian Process Smoother with an exact solution computed using a hybrid Monte Carlo approach to path sampling, applied to a stochastic double well potential model. It is demonstrated that the variational smoother provides us a very accurate estimate of mean path while marginal variance is slightly underestimated. We conclude with some remarks as to the advantages and disadvantages of the variational smoother.

Original language	English
Title of host publication	Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP
Publisher	IEEE
Pages	306-311
Number of pages	6
ISBN (Print)	1424415667, 9781424415663
DOIs	https://doi.org/10.1109/MLSP.2007.4414324
Publication status	Published - 1 Dec 2007
Event	17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007 - Thessaloniki, United Kingdom Duration: 27 Aug 2007 → 29 Aug 2007

Conference

Conference	17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007
Country/Territory	United Kingdom
City	Thessaloniki
Period	27/08/07 → 29/08/07

Bibliographical note

© 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Access to Document

10.1109/MLSP.2007.4414324

mlsp_ys07
© 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 108 KB

Cite this

Shen, Y., Archambeau, C., Cornford, D., Opper, M., Shawe-Taylor, J., & Barillec, R. (2007). Evaluation of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems. In Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP (pp. 306-311). Article 4414324 IEEE. https://doi.org/10.1109/MLSP.2007.4414324

@inproceedings{f2f8cd2fabce4eeeacadc265486181fd,

title = "Evaluation of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems",

abstract = "In recent work we have developed a novel variational inference method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the Variational Gaussian Process Smoother with an exact solution computed using a hybrid Monte Carlo approach to path sampling, applied to a stochastic double well potential model. It is demonstrated that the variational smoother provides us a very accurate estimate of mean path while marginal variance is slightly underestimated. We conclude with some remarks as to the advantages and disadvantages of the variational smoother.",

author = "Y. Shen and C. Archambeau and D. Cornford and M. Opper and J. Shawe-Taylor and R. Barillec",

note = "{\textcopyright} 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ; 17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007 ; Conference date: 27-08-2007 Through 29-08-2007",

year = "2007",

month = dec,

day = "1",

doi = "10.1109/MLSP.2007.4414324",

language = "English",

isbn = "1424415667",

pages = "306--311",

booktitle = "Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP",

publisher = "IEEE",

address = "United States",

}

Shen, Y, Archambeau, C, Cornford, D, Opper, M, Shawe-Taylor, J & Barillec, R 2007, Evaluation of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems. in Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP., 4414324, IEEE, pp. 306-311, 17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007, Thessaloniki, United Kingdom, 27/08/07. https://doi.org/10.1109/MLSP.2007.4414324

Evaluation of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems. / Shen, Y.; Archambeau, C.; Cornford, D. et al.
Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP. IEEE, 2007. p. 306-311 4414324.

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - Evaluation of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

AU - Shen, Y.

AU - Archambeau, C.

AU - Cornford, D.

AU - Opper, M.

AU - Shawe-Taylor, J.

AU - Barillec, R.

N1 - © 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2007/12/1

Y1 - 2007/12/1

N2 - In recent work we have developed a novel variational inference method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the Variational Gaussian Process Smoother with an exact solution computed using a hybrid Monte Carlo approach to path sampling, applied to a stochastic double well potential model. It is demonstrated that the variational smoother provides us a very accurate estimate of mean path while marginal variance is slightly underestimated. We conclude with some remarks as to the advantages and disadvantages of the variational smoother.

AB - In recent work we have developed a novel variational inference method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the Variational Gaussian Process Smoother with an exact solution computed using a hybrid Monte Carlo approach to path sampling, applied to a stochastic double well potential model. It is demonstrated that the variational smoother provides us a very accurate estimate of mean path while marginal variance is slightly underestimated. We conclude with some remarks as to the advantages and disadvantages of the variational smoother.

UR - http://www.scopus.com/inward/record.url?scp=48149106949&partnerID=8YFLogxK

UR - https://ieeexplore.ieee.org/document/4414324

U2 - 10.1109/MLSP.2007.4414324

DO - 10.1109/MLSP.2007.4414324

M3 - Conference publication

AN - SCOPUS:48149106949

SN - 1424415667

SN - 9781424415663

SP - 306

EP - 311

BT - Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP

PB - IEEE

T2 - 17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007

Y2 - 27 August 2007 through 29 August 2007

ER -

Shen Y, Archambeau C, Cornford D, Opper M, Shawe-Taylor J, Barillec R. Evaluation of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems. In Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP. IEEE. 2007. p. 306-311. 4414324 doi: 10.1109/MLSP.2007.4414324

Evaluation of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

Abstract

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this